Methods, systems, articles of manufacture and apparatus to remap household identification

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed to reduce a remapping error. An example apparatus includes a distance matrix generator to generate a distance matrix corresponding to a first household demographic model and a second household demographic model. The example apparatus also includes a distance matrix identifier to determine a reassignment distance of the distance matrix. The example apparatus also includes a person identification assigner to, in response to the reassignment distance being less than a reassignment threshold, assign a person identification number of a first person of the first household to a second person of the second household based on the distance matrix.

RELATED APPLICATION

This patent arises from a continuation of Provisional U.S. PatentApplication Ser. No. 62/947,352, which was filed on Dec. 12, 2019.Provisional U.S. Patent Application Ser. No. 62/947,352 is herebyincorporated herein by reference in its entirety. Priority toProvisional U.S. Patent Application Ser. No. 62,947,352 is herebyclaimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to household identification mapping,and, more particularly, to methods, systems, articles of manufacture andapparatus to remap household identification.

BACKGROUND

In recent years, audience measurement entities (AMEs) have developedhousehold demographic models. Household demographic models often includeindividual level characterization, including assigning personidentification numbers and corresponding features to each individual ofa household. AMEs may remodel household demographic models over time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example household identification mapping systemconstructed in accordance with the teachings of this disclosure to remaphousehold identification.

FIG. 2 is a block diagram of an example household remodeler of FIG. 1 toremap household identification.

FIGS. 3A-3B are diagrams representative of an example householddemographic model.

FIGS. 4A-4B are diagrams representative of an example weighted householddemographic model of FIGS. 3A-B.

FIGS. 5-6 are diagrams representative of example distance matrices ofthe household of FIGS. 3A-B.

FIGS. 7A-7B are diagrams representative of an example reassignedhousehold demographic model of FIGS. 3A-3B

FIGS. 8-9 are flowcharts representative of example methods that may beexecuted by the example household remodeler of FIGS. 1 and/or 2 toremodel a household.

FIG. 10 is a block diagram of an example processing platform structuredto execute machine readable instructions to implement the methods ofFIGS. 8-9 and/or the example household remodeler of FIGS. 1 and/or 2.

The figures are not to scale. Instead, the thickness of the layers orregions may be enlarged in the drawings. In general, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

Descriptors “first,” “second,” “third,” etc. are used herein whenidentifying multiple elements or components which may be referred toseparately. Unless otherwise specified or understood based on theircontext of use, such descriptors are not intended to impute any meaningof priority, physical order or arrangement in a list, or ordering intime but are merely used as labels for referring to multiple elements orcomponents separately for ease of understanding the disclosed examples.In some examples, the descriptor “first” may be used to refer to anelement in the detailed description, while the same element may bereferred to in a claim with a different descriptor such as “second” or“third.” In such instances, it should be understood that suchdescriptors are used merely for ease of referencing multiple elements orcomponents.

DETAILED DESCRIPTION

In recent years, the need for household demographic remodeling has risenin the audience measurement realm. Household demographic models allowaudience measurement entities (AMEs) to characterize individualhouseholds for media monitoring. The household demographic models maycharacterize each individual of the household with person identificationnumbers, for which each individual is associated with any number ofcharacteristics (e.g., features, etc.), such as age, gender, etc. Suchmodeling allows the AMEs to more specifically and/or otherwiseaccurately credit media events (e.g., media consumption instances) tocertain demographics.

However, a household may change over time, causing the previouslygenerated household demographic model to less accurately represent theindividuals of the household. AMEs may also consider changes in marketlevel data (e.g., demographic data of one or more households), which mayreflect the household demographic models. For example, market level datamay include data of one or more households of a certain geographicregion, household data of a third-party, etc. In some examples, the AMEmay determine whether the household reflects and/or matches the marketlevel data. For example, individuals may move out of a household,individuals may change age range, etc. This creates a need to remodelthe households to achieve household level accuracy and market leveldistribution accuracy. As used herein, “remodeling” a household refersto reassigning (e.g., remapping) person identification numbers from afirst household (e.g., original model) to a second household (e.g.,updated model).

Existing methods of household remodeling often assign personidentification numbers in an inconsistent and/or otherwise arbitraryway. Such inconsistent remodeling efforts are typically guided by one ormore market analysts and/or other market research personnel. Even if afirst market analyst manages to design a remapping strategy in arelatively accurate manner, a second market analyst will still have thediscretionary freedom to apply alternate remapping strategies. Suchdiscretionary behavior results in inconsistent results across and/orwithin market geographies of interest. This also creates inconsistenciesin household data analysis (e.g., monitoring media) for the samehousehold over time. That is, an arbitrary reassignment of personidentification numbers can create errors and/or generate unnecessaryvariability between household demographic models. Additionally, toproperly represent a household at a person-level, person identificationnumbers should consistently map to the most similar person in thathousehold from a previously remodeled representation of that household.Improving the consistency of identification number mapping reduces theremapping error and overall variation of demographics in a remodeleffort.

In the illustrated example of FIG. 1, a household identification mappingsystem 100 includes an example original household 102, an examplemodified household 102 b, an example household database 104, an examplereference database 106, an example network 108, an example computingdevice 110, and an example household remodeler 112.

In the illustrated example of FIG. 1, the original household 102includes individuals of the original household 102. In some examples,the original household 102 is the first, current, etc. household. Insome examples, the individuals associated with the original household102 are residents, visitors, etc. As disclosed herein, the originalhousehold 102 is a return path data (RPD) household. That is, RPDhouseholds use one or more devices (e.g., set-top-boxes (STBs)) toobtain media from a media distributor, and those same devices facilitatean ability to send some data back to the distributor(s). In someexamples, the RPD households (HHs) are panelist households, and in someexamples the RPD tills are not associated with panelist cultivationactivities. While one original household 102 is illustrated in FIG. 1,the household identification mapping system 100 may include any numberof original households 102.

In the illustrated example of FIG. 1, the modified household 102 bincludes individuals of the modified household 102 b. In examplesdisclosed herein, the modified household 102 b is the original household102 at a second time period. That is, the modified household 102 b is anupdated (e.g., remodeled) version of the original household 102. In someexamples, the modified household 102 b is the original household 102 amonth later, a year later, etc. The modified household 102 b may includesome or all of the individuals of the original household 102.Additionally or alternatively, the modified household 102 b may includeadditional individuals not included in the original household 102. Forexample, the original household 102 may include a first individual(e.g., a father), a second individual (e.g., a mother), and a thirdindividual (e.g., a high school senior). The modified household 102 bmay include the first individual (e.g., the father), the secondindividual (e.g., the mother), and a fourth individual (e.g., agrandparent). The modified household 102 b does not include the thirdindividual (e.g., the high school senior may have moved to college,etc.) and includes the new fourth individual (e.g., the grandparent mayhave moved into the original household 102). Thus, the modifiedhousehold 102 b represents the original household 102 at a later time.

In the illustrated example of FIG. 1, the household database 104 storesdata associated with the original household 102 and/or the modifiedhousehold 102 b. For example, the household database 104 may storehousehold demographic model(s) of the original household 102 and/or themodified household 102 b, including person identification numbers andfeatures of the individuals associated with the original household 102and modified household 102 b. In some examples, the household database104 stores both an original household demographic model (e.g., a firstor otherwise temporally earlier version of a household demographicmodel) of the original household 102 and a modified householddemographic model (e.g., a second or otherwise temporally subsequentversion of the household demographic model, in which one or morehousehold person level changes have occurred) of the modified household102 b.

In the illustrated example of FIG. 1, the reference database 106 storesdata associated with a market of interest, a geographic region ofinterest, etc. For example, the reference database 106 stores marketlevel data characterizing one or more original households 102.

In the illustrated example of FIG. 1, the network 108 facilitatescommunication between the original household 102, the modified household102 b, the household database 104, the reference database 106, and/orthe computing device 110. In some examples, any number of originalhouseholds 102 and/or modified households 102 b can be communicativelycoupled to the reference database 106 and the computing device 110 viathe network 108. The communication provided by the network 108 can bevia, for example, the Internet, an Ethernet connection, USB cable, etc.

In the illustrated example of FIG. 1, the computing device 110communicates with the original household 102, the modified household 102b, the household database 104, and the reference database 106 throughthe network 108. In some examples, the computing device 110 contains thehousehold remodeler 112. In the illustrated example of FIG. 1, thecomputing device 110 is a server, but alternatively may be an Internetgateway, a laptop, a cellular phone, a tablet, etc.

In the illustrated example of FIG. 1, the household remodeler 112creates a set of distance matrices associated with the household datastored in the household database 104. The household remodeler 112 mayobtain the household demographic model of the original household 102. Insome examples, the household remodeler 112 may determine to remap (e.g.,reassign, etc.) the people identification numbers of the originalhousehold 102 to a modified household demographic model (e.g., thehousehold demographic model of the modified household 102 b). Forexample, the household remodeler 112 may detect a person level change inthe original household 102, may be configured to update the personidentification number(s) periodically (e.g., every month, every year,etc.), etc. In response to the determination to update the originalhousehold, the household remodeler 112 obtains the original householddemographic model of the original household 102 and the modifiedhousehold demographic model of the modified household 102 b. Thehousehold remodeler 112 determines the distance matrix associated withassigning people identification numbers from the original household tothe most similar individual of the modified household. That is, thehousehold remodeler 112 generates a reassigned household demographicmodel (e.g., the person identification numbers of the original householdassigned to the individuals of the modified household). In someexamples, the household remodeler 112 is an application-specificintegrated circuit (ASIC), and in some examples the household remodeler112 is a field programmable gate array (FPGA). Alternatively, thehousehold remodeler 112 can be software located in the firmware of thecomputing device 110.

In the illustrated example of FIG. 2, the household remodeler 112includes an example model accessor 202 to access the householddemographic model(s) of the original household 102 and modifiedhousehold 102 b stored in the household database 104 and/or thereference database 106. In some examples, the model accessor 202includes means for model accessing (sometimes referred to herein as amodel accessing means). The example means for model accessing ishardware. In some examples, the model accessor 202 accesses thehousehold database 104 and/or the reference database 106 content inresponse to a query, on a manual basis, on a periodic basis, or on ascheduled basis. For example, the model accessor 202 may access thehousehold database 104 and/or the reference database 106 once a month,once a quarter, once a year, etc. to remodel the original household 102.

In the illustrated example of FIG. 2, the household remodeler 112includes the data formatter 204 to format the household demographicmodel(s) accessed by the model accessor 202. In some examples, the dataformatter 204 includes means for formatting (sometimes referred toherein as a formatting means). The example means for formatting ishardware. That is, the data formatter 204 formats the features withinthe household demographic models. For example, one feature of ahousehold demographic model may be the individual's age. The dataformatter 204 formats the age of an individual into age buckets (e.g.,ranges). For example, the data formatter 204 formats the age of asix-year-old individual to the 0-12 age bucket. However, the dataformatter 204 may additionally or alternatively format the age to a moregranular range (e.g., 0-6, etc.) or a less granular range (e.g., 0-18,etc.). In other examples, the data formatter 204 formats a feature intoa Boolean (e.g., binary) data type, such as zero or one. For example,gender may be assigned zero for a female indication and one for a maleindication. In some examples, the data formatter 204 generates an adulthousehold demographic model and a children household demographic model(e.g., the data formatter 204 separates the adults and children of ahousehold demographic model). For example, the data formatter 204 maygenerate an adult household demographic model of the individuals in thehousehold demographic model that are at least 18 years old.

In the illustrated example of FIG. 2, the household remodeler 112includes the feature weight assigner 206 to assign weights to thefeatures of a household demographic model. In some examples, the featureweight assigner 206 includes means for weight assigning (sometimesreferred to herein as a weight assigning means). The example means forweight assigning is hardware. For example, the feature weight assigner206 may assign each age bucket a value (e.g., the 0-12 age bucketassigned a weight of one, the 13-17 age bucket assigned a weight of two,the 18-24 age bucket assigned a weight of three, etc.), the genderfeature a weight of 0.9, the head of household feature a weight of 0.05,etc. In some examples, the feature weights determine how the householdidentification is remapped. That is, the feature weights determine whichfeature defines similarity of individuals between an original householdand an updated household. In the example described above, the agefeature is assigned the highest weight. Thus, in this example, thehousehold identification mapping is based primarily on age (e.g.,individuals are remapped primarily according to age). In some examples,the feature weights are user defined. In some examples, the featureweights serve as a tiebreaker between two similar individuals. That is,differently weighted features may reduce the likelihood of twoindividuals of the original household having the same distance from oneindividual of the remodeled household.

In the illustrated example of FIG. 2, the household remodeler 112includes a distance matrix generator 208 to generate distance matricesbetween the original household demographic model and the modifiedhousehold demographic model (e.g., based on an update trigger (e.g., anupdate trigger scheduled for each month, etc.)). In some examples, thedistance matrix generator 208 includes means for generating a distancematrix (sometimes referred to herein as a distance matrix generatingmeans). The example means for generating a distance matrix is hardware.The distance matrix generator 208 determines the absolute differencebetween the person feature weight of the original household demographicmodel and the person feature weight of the modified householddemographic model. As used herein, the absolute difference between theperson feature weight of the original household demographic model andthe person feature weight of the modified household demographic model isreferred to as a “remapping cost”. The remapping cost represents thecost of remapping the person identification number of an individual ofthe original household to an individual of the modified household. Thatis, the remapping cost can define the similarity between the individualof the original household and the individual of the modified household.For example, a relatively smaller remapping cost represents theindividual of the original household is more similar to the individualof the modified household with respect to a relatively larger remappingcost.

Respective ones of the distance matrices represent multiple remappingcosts of remapping a person identification number of an individual fromthe original household to an individual of the modified household. Inexamples disclosed herein, the remapping cost is the Manhattan distancebetween individuals of the original household and the modified household(e.g., the sum of the absolute difference between each feature of thehousehold demographic models). In some examples, the distance matrixgenerator 208 generates a distance matrix for every possible combinationof person identification reassignments (e.g., remapping combination).That is, if there are N individuals in the original household and Mindividuals in the modified household, the distance matrix generator 208generates N factorial (e.g., N!) distance matrices if N is equal to M, Npermutations of M (e.g., N! divided by M!) distance matrices if N isgreater than M, or M permutations of N (e.g., M! divided by N!) distancematrices if M is greater than N.

In examples disclosed herein, the distance matrix generator 208permutates the rows of a distance matrix to generate additional distancematrices (e.g., different remapping assignments between individuals ofthe original household and individuals of the modified household). Thatis, the distance matrix generator 208 determines remapping rows (e.g.,the remapping cost of reassigning the person identification number ofindividual i of the original household to each of the individuals j ofthe modified household). Thus, the example distance matrix generator 208permutates the remapping rows to generate one or more additionaldistance matrices (e.g., switch first and second remapping rows, makefirst remapping row last remapping row, etc.). Additionally oralternatively, the distance matrix generator 208 can permutate thecolumns of a distance matrix.

The example distance matrix generator 208 may also determine whether togenerate separate adult and/or children distance matrices based onwhether the number of distance matrices exceeds a combination threshold.For example, when the number of distance matrices exceeds thecombination threshold, it may be too computationally expensive (e.g.,processing time, storage space, etc.) to analyze each distance matrix.Thus, the example distance matrix generator 208 generates adult distancematrices (e.g., remapping costs between adults of the original householdand modified household) and separate children distance matrices (e.g.,remapping costs between children of the original household and modifiedhousehold). For example, the original household includes eight totalindividuals with four adults and four children and the modifiedhousehold includes eight total individuals with four adults and fourchildren. If the distance matrix generator 208 generated distancematrices including both adults and children, the distance matrixgenerator 208 might generate 40,320 distance matrices (e.g., 8!=40,320).However, if the distance matrix generator 208 generated adult distancematrices and separate children distance matrices, the distance matrixgenerator 208 might generate 48 distance matrices (e.g., 4!+4!=48).

In the illustrated example of FIG. 2, household remodeler 112 includesthe distance matrix identifier 210 to identify the distance matrix witha reassignment distance that is less than a reassignment threshold. Insome examples, the distance matrix identifier 210 includes means foridentifying a reassignment distance (sometimes referred to herein as areassignment distance identifying means). The example means foridentifying a reassignment distance is hardware. That is, the distancematrix identifier 210 identifies the reassignment distance matrix. Asused herein, the “reassignment distance matrix” is the distance matrixthat determines the assignments of the person identification numbers ofthe individuals of the original household to the individuals of themodified household. In some examples, the reassignment distance of eachdistance matrix is the diagonal sum of the remapping costs betweenindividuals of the original household demographic model and the modifiedhousehold demographic model (e.g., the sum of the values along thediagonal of the distance matrix). In some examples, the diagonal sum ofdistances of a distance matrix is the sum of the values of the maindiagonal of the matrix (e.g., principal diagonal, primary diagonal,leading diagonal, major diagonal, etc.). That is, for a distance matrixA with i number of rows, j number of columns, and entries A_(i,j) (e.g.,the remapping cost between a first individual of the original householdand a second individual of the modified household), the main diagonal ofthe distance matrix A includes the entries A_(i,j) where i=j.

As disclosed herein, the assignment distance matrix represents anacceptable cost path for person identification number reassignment usingweighted features. That is, the assignment distance matrix representsperson identification number reassignments between the most similarpeople (e.g., defined based on the relative weighting of features) ofthe original household demographic model and the modified householddemographic model. In some examples, the distance matrix identifier 210identifies and/or otherwise represents a distance matrix that has areassignment distance less than the reassignment threshold. In someexamples, the distance matrix identifier 210 analyzes every distancematrix (e.g., all possible reassignments of the people identificationnumbers of the individuals of the original household to the individualsof the modified household) and selects the distance matrix with theminimum reassignment distance.

In the illustrated example of FIG. 2, the household remodeler 112includes the person identification assigner 212 to assign the personidentification numbers of the original household demographic model tothe individuals of the modified household demographic model. In someexamples, the person identification assigner 212 includes means forperson assigning (sometimes referred to herein as a person assigningmeans). The example means for person assigning is hardware. As disclosedherein, the person identification assigner 212 assigns the personidentification numbers based on the reassignment distance matrixidentified by the distance matrix identifier 210. That is, using theexample distance matrix A described above, the person identificationassigner 212 assigns the person identification number of individual i ofthe original household to individual j of the modified household wherei=j.

In examples disclosed herein, the reassignment distance matrixidentified by the distance matrix identifier 210 (e.g., a distancematrix with an acceptable cost path) may not map every individual of theoriginal household to the most similar individual of the modifiedhousehold. For example, the original household may include two children(e.g., individuals in the 0-12 age bucket) and the modified householdmay include only one child. Thus, only one of the people identificationnumbers of the children of the original household can be reassigned tothe child of the modified household (e.g., the person identificationnumber of the second child will not be remapped to the most similarperson of the modified household). However, methods, systems, articlesof manufacture, and apparatus disclosed herein remap individuals basedon weighted features that, when changed or remapped, have less negativeinfluence on accuracy and/or industry expectations compared to otherwisearbitrarily remapping individuals.

In the illustrated example of FIG. 2, the household remodeler 112includes the person identification database 214 to store the reassignedhousehold demographic model generated by the person identificationassigner 212 (e.g., the modified household demographic model withreassigned person identification numbers). In some examples, the personidentification database 214 stores the original household demographicmodel, the modified household demographic model, and the reassignedhousehold demographic model. In some examples, the person identificationdatabase 214 only stores the reassigned household demographic model.

While an example manner of implementing the household remodeler 112 ofFIG. 1 is illustrated in FIGS. 1 and 2, one or more of the elements,processes and/or devices illustrated in FIGS. 1 and 2 may be combined,divided, re-arranged, omitted, eliminated and/or implemented in anyother way. Further, the example model accessor 202, the example dataformatter 204, the example feature weight assigner 206, the exampledistance matrix generator 208, the example distance matrix identifier210, the example person identification assigner 212 and/or, moregenerally, the example household remodeler 112 of FIGS. 1 and 2 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample model accessor 202, the example data formatter 204, the examplefeature weight assigner 206, the example distance matrix generator 208,the example distance matrix identifier 210, the example personidentification assigner 212 and/or, more generally, the examplehousehold remodeler 112 could be implemented by one or more analog ordigital circuit(s), logic circuits, programmable processor(s),programmable controller(s), graphics processing unit(s) (GPU(s)),digital signal processor(s) (DSP(s)), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example, modelaccessor 202, the example data formatter 204, the example feature weightassigner 206, the example distance matrix generator 208, the exampledistance matrix identifier 210, the example person identificationassigner 212 and/or the example household remodeler 112 is/are herebyexpressly defined to include a non-transitory computer readable storagedevice or storage disk such as a memory, a digital versatile disk (DVD),a compact disk (CD), a Blu-ray disk, etc. including the software and/orfirmware. Further still, the example household remodeler 112 of FIGS. 1and/or 2 may include one or more elements, processes and/or devices inaddition to, or instead of, those illustrated in FIGS. 1 and 2, and/ormay include more than one of any or all of the illustrated elements,processes and devices. As used herein, the phrase “in communication,”including variations thereof, encompasses direct communication and/orindirect communication through one or more intermediary components, anddoes not require direct physical (e.g., wired) communication and/orconstant communication, but rather additionally includes selectivecommunication at periodic intervals, scheduled intervals, aperiodicintervals, and/or one-time events.

FIG. 3A is a diagram representative of an example original householddemographic model 300. The original household demographic model 300includes person identification numbers 302 and a plurality of features(e.g., age bucket 304, gender 306, head of household flag 308, and longterm visitor flag 310). However, household demographic models disclosedherein may include any number and/or type of features, such as income,type of media consumed, ethnicity, etc. The original householddemographic model 300 includes feature data for eight individuals (e.g.,the first individual 312, the second individual 314, the thirdindividual 316, the fourth individual 318, the fifth individual 320, thesixth individual 322, the seventh individual 324, and the eighthindividual 326). For example, the first individual 312 has a personidentification number of 1, is in the 55-64 age bucket, is a female, isnot the head of the household, and is not a long term visitor. Inanother example, the fourth individual 318 has a person identificationnumber of 4, is in the 25-34 age bucket, is a male, is not head of thehousehold, and is not a long term visitor. In the illustrated example ofFIG. 3A, the original household demographic model 300 is partiallyformatted. That is, the data of the age bucket 304 feature has beenformatted into a particular range. For example, the first individual 312may be 60 years old. The data formatter 204 formats this feature of thefirst individual 312 into the age bucket 55_64.

FIG. 3B is a diagram representative of an example modified householddemographic model 350. The modified household demographic model 350 isthe modified model of the original household demographic model 300. Thatis, the modified household demographic model 350 models or otherwiserepresents the same household as the original household demographicmodel 300, but further represents differences in that household that mayhave occurred over some time (e.g., a month, a quarter, a year, etc.).For example, the modified household demographic model 350 may be theoriginal household demographic model 300 updated a year later. Themodified household demographic model 350 includes person identificationnumbers 302 and the same plurality of features (e.g., age bucket 304,gender 306, head of household flag 308, and long term visitor flag 310)as the original household demographic model 300. However, the modifiedhousehold demographic model 350 may additionally or alternativelyinclude features different than those of the original householddemographic model 300. The modified household demographic model 350includes seven individuals (e.g., the first individual 352, the secondindividual 354, the third individual 356, the fourth individual 358, thefifth individual 360, the sixth individual 362, and the seventhindividual 364). In one example, the first individual 352 has a personidentification number of 1, is in the age bucket 25-34, is male, is notthe head of household, and is not a long term visitor. While theoriginal household demographic model 300 and the modified householddemographic model 350 model the same household, at least one of thehousehold members is now associated with alternate information, and themodified household demographic model 350 has been updated to reflectchanges to that household. For example, the modified householddemographic model 350 includes seven individuals compared to the eightindividuals included in the original household demographic model 300.This scenario may occur due to an individual of the original household(e.g., the original household 102 of FIG. 1) moving away, a collegestudent returned home for the summer, an elderly parent moved into anursing home, etc.

While the first individual 352 of FIG. 3B and the first individual 312of FIG. 3A are both assigned the person identification number 1, they donot have the same feature data. For example, the first individual 312corresponding to the original household demographic model 300 is in theage bucket 55-64 while the first individual 352 corresponding to themodified household demographic model 350 is in the age bucket 25-34.Thus, the first individual 312 of the original household is not the sameperson as the first individual 352 of the modified household. In thisexample, the person identification number reassignment (e.g., remapping)of the first individual 352 from the original household demographicmodel 300 to the modified household demographic model 350 is notreassigning person identification numbers to similar individuals. Forexample, the fourth individual 318 with a person identification numberof 4 is in the 25-34 age bucket, is a male, is not the head of thehousehold, and is not a long term visitor. Thus, a remapping to reducevariation between an original and modified household may instead assignthe person identification number of the fourth individual 318 to thefirst individual 352.

FIG. 4A is a diagram representative of an example original weightedhousehold demographic model 400. In the illustrated example, theoriginal weighted household demographic model 400 is a weighted versionof the original household demographic model 300 of FIG. 3A. That is, theoriginal weighted household demographic model 400 includes the personidentification numbers 302 and the same plurality of features (e.g., agebucket 304, gender 306, head of household flag 308, and long termvisitor flag 310) as the original household demographic model 300. Theoriginal weighted household demographic model 400 includes feature datafor the same eight individuals (e.g., the first individual 312, thesecond individual 314, the third individual 316, the fourth individual318, the fifth individual 320, the sixth individual 322, the seventhindividual 324, and the eighth individual 326) as the original householddemographic model 300. In some examples, the feature weight assigner 206of FIG. 2 generates the original weighted household demographic model400. For example, the age bucket feature 304 does not include dataindicating age ranges (e.g., 0_12, 18_24, 25_34, and 55_64 illustratedin FIGS. 3A-3B). Instead, the age bucket feature 304 of FIG. 4A has beenweighted. For example, the age bucket 55_64 has been assigned a weightof 7, the age bucket range 25_34 has been assigned a weight of 4, etc.In another example, the long term visitor feature 310 has a weight of0.04. In other words, if the individual is a long term visitor (e.g.,the eighth individual 326), they are assigned a weight of 0.04, while anindividual that is not a long term visitor (e.g., the first individual312, the second individual 314, etc.) is assigned a weight of 0.

As described above, the weight of the features determines how similarityof individuals is defined. For example, the age bucket 304 feature hasthe highest relative weight(s) (e.g., 1, 3, 4, 7) while the long termvisitor flag 310 feature has the lowest weight (e.g., 0.04). Thus,individuals are considered more similar if they are closer in age (e.g.,the first individual 312 corresponding to the example original householddemographic model 300, which is associated with the age range 55_64 ismore similar to first individual 352 corresponding to the examplemodified household demographic model 350, which is associated with theage range of 25_34 than the fifth individual 360 corresponding to theexample modified household demographic model 350 which is associatedwith the age range of 0_12).

FIG. 4B is a diagram representative of an example modified weightedhousehold demographic model 450. In the illustrated example, themodified weighted household demographic model 450 is a weighted versionof the modified household demographic model 350 of FIG. 3B. That is, themodified weighted household demographic model 450 includes the personidentification numbers 302 and the same plurality of features (e.g., agebucket 304, gender 306, head of household flag 308, and long termvisitor flag 310) as the modified household demographic model 350. Themodified weighted household demographic model 450 includes feature datafor the same seven individuals (e.g., the first individual 352, thesecond individual 354, the third individual 356, the fourth individual358, the fifth individual 360, the sixth individual 362, and the seventhindividual 364) as the modified household demographic model 350. In someexamples, the feature weight assigner 206 of FIG. 2 generates themodified weighted household demographic model 450.

In some examples, the feature weight assigner 206 assigns the featuresof the modified weighted household demographic model 450 the sameweights as the original weighted household demographic model 400. Forexample, the age bucket 304 may be assigned the relatively highestweight (e.g., 1, 2, 3, 4, 5, 6, 7) and the long term visitor feature 310may be assigned the relatively lowest weight (e.g., 0.04). However, thefeature weight assigner 206 may assign the features of the modifiedweighted household demographic model 450 different weights than thefeatures of the original weighted household demographic model 400.

FIG. 5 is a diagram representative of an example distance matrix 500. Insome examples, the distance matrix generator 208 generates the distancematrix 500. The rows of the distance matrix 500 represent theindividuals of the original household (e.g., the individuals illustratedin the original household demographic model 300 and the originalweighted household demographic model 400). In the illustrated example ofFIG. 5, the rows are labeled with the person identification numbers 302of the original household. The columns of the distance matrix 500represent the individuals of the modified household (e.g., theindividuals illustrated in the modified household demographic model 350and the modified weighted household demographic model 450). In theillustrated example of FIG. 5, the columns are labeled with the personidentification numbers 302 of the modified household. While the rows ofthe example distance matrix 500 represent individuals of the originalhousehold and the columns of the distance matrix 500 representindividuals of the modified household, in other examples the rows of thedistance matrix 500 can represent individuals of the modified householdand the columns of the distance matrix 500 can represent individuals ofthe original household.

The distance matrix 500 illustrates the remapping cost of everycombination of reassigning the person identification numbers from thefirst household (e.g., the original household) to the individuals of thesecond household (e.g., the modified household). That is, one or moredistance matrices may represent every combination of pairs ofindividuals between the original and modified households. In theillustrated example of FIG. 5, the remapping cost is the Manhattandistance between an individual of the original household and anindividual of the modified household. For example, the distance matrixelement 502 is the remapping cost of assigning the person identificationnumber of the first individual 312 (e.g., 1) to the first individual 352of the modified household. The distance matrix element 502 has aremapping cost of 3.9, which is determined by the example distancematrix generator 208 based on the absolute differences of cell valuesfor the original and modified models (e.g.,|7−4|+|0−0.9|+|0−0|+|0−0|=3.9).

The example distance matrix 500 has a reassignment distance 504 of 13.6(e.g., 3.9+0+2.9+2.9+0+0+3.9=13.6). That is, the reassignment distanceto assign the person identification number of the first individual 312to the first individual 352, the person identification number of thesecond individual 314 to the second individual 354, the personidentification number of the third individual 316 to the thirdindividual 356, the person identification number of the fourthindividual 318 to the fourth individual 358, the person identificationnumber of the fifth individual 320 to the fifth individual 360, theperson identification number of the sixth individual 322 to the sixthindividual 362, and the person identification number of the seventhindividual 324 to the seventh individual 364 is 13.6. The reassignmentof person identification numbers between households of the distancematrix 500 is determined based on a main diagonal 506 of the exampledistance matrix 500. Thus, the reassignment distance 504 is the sum ofthe distance matrix elements (e.g., remapping costs) of the maindiagonal 506.

In the illustrated example of FIG. 5, the distance matrix 500 has agreater number of rows (e.g., 8 rows) than columns (e.g., 7 columns).That is, there are less columns than rows due to the modified householdhaving less individuals than the original household. For example, duringthe time period between the original household and the modifiedhousehold, one or more individuals of the original household may havemoved out (e.g., a student moving to college, a grandparent moving to anursing home, etc.). The eighth row corresponding to the eighthindividual 326 is not included in the main diagonal 506 and, thus, thereassignment distance 504 does not depend on the eighth individual 326.In other words, the person identification number of the eighthindividual 326 is not assigned to any individual of the modifiedhousehold (e.g., the individuals of the modified household demographicmodels 350, 450).

FIG. 6 is a diagram representative of an example second distance matrix600. The second distance matrix 600 is a second distance matrixrepresenting a second remapping of the original household (e.g., theoriginal household demographic models 300, 400) to the modifiedhousehold (e.g., the modified household demographic models 350, 450).The second distance matrix 600 may be generated to determine a secondreassignment distance 602. In some examples, the distance matrixgenerator 208 generates the second distance matrix 600. In someexamples, the distance matrix generator 208 generates any combination ofdistance matrices. In some other examples, the distance matrix generator208 generates the second distance matrix 600 in response to the firstdistance matrix 500 having a reassignment distance greater than thereassignment threshold. As described above with reference to the exampledistance matrix 500 of FIG. 5, the rows of the second distance matrix600 represent individuals of the original household (e.g., before aremodel) and the columns of the second distance matrix 600 representindividuals of the modified household (e.g., after a remodel). In theillustrated example of FIG. 6, the distance matrix generator 208determined a second permutation of the rows of the first distance matrix500. For example, the first row of the first distance matrix 500 is notincluded in the second distance matrix 600. The original householdincludes eight individuals and the modified household includes sevenindividuals (e.g., a child of the original household moves out tocollege, etc.). Thus, one of the person identification numbers of theoriginal household will not get assigned to an individual of themodified household. In the illustrated example of FIG. 6, the personidentification number of the first individual 312 is not reassigned toan individual of the modified household. Thus, the first row of thefirst distance matrix 500 is not included in the second distance matrix600. In the illustrated example of FIG. 6, the second row of the firstdistance matrix 500 is the second row of the second distance matrix 600,the third row of the first distance matrix 500 is the seventh row of thesecond distance matrix 600, etc.

The example second distance matrix 600 of FIG. 6 has a reassignmentdistance 602 of 2.04. That is, the sum of a main diagonal 604 is 2.04(e.g., 0+0+1+1+0+0+0.04). Thus, the reassignment distance to assign theperson identification number of the fourth individual 318 (e.g., personidentification number 4) to the first individual 352, the personidentification number of the second individual 314 (e.g., the personidentification number 2) to the second individual 354, the personidentification number of the seventh individual 324 (e.g., the personidentification number 7) to the third individual 356, the personidentification number of the eighth individual 326 (e.g., the personidentification number 8) to the fourth individual 358, the personidentification number of the fifth individual 320 (e.g., the personidentification number 5) to the fifth individual 360, the personidentification number of the sixth individual 322 (e.g., the personidentification number 6) to the sixth individual 362, and the personidentification number of the third individual 316 (e.g., the personidentification number 3) to the seventh individual 364 is 2.04. Thefirst individual 312 (e.g., the person identification number 1) is notillustrated in the second distance matrix 600. Thus, the personidentification number 1 is not assigned to an individual of the updatedhousehold.

The reassignment distance 602 of the second distance matrix 600 of FIG.6 (e.g., 2.04) is less than the reassignment distance 504 of thedistance matrix 500 of FIG. 5 (e.g., 13.6). In other words, thereassignments defined in the second distance matrix 600 result in moresimilar remappings (i.e., lower distance magnitude value) compared tothe reassignments defined in the distance matrix 500 (i.e., a relativelyhigher distance magnitude value). In examples disclosed herein, thedistance matrix identifier 210 of FIG. 2 may select the second distancematrix 600 in response to the second distance matrix 600 having arelatively lower reassignment distance than the distance matrix 500(e.g., the reassignment distance matrix is the second distance matrix600). Thus, the person identification assigner 212 may reassign theperson identification numbers 302 of the original household to theindividuals of the modified household according to the main diagonal 604of the second distance matrix 600.

In the illustrated example of FIGS. 5 and 6, the example distance matrixgenerator 208 generated two distance matrices (e.g., the distance matrix500 of FIG. 5 and the second distance matrix 600 of FIG. 6). The exampledistance matrix identifier 210 determined the second distance matrix 600has a reassignment distance (e.g., the reassignment distance 602) thatis an acceptable cost (e.g., the reassignment distance 602 is less thanthe reassignment threshold). Thus, the example person identificationassigner 212 may assign the person identification numbers of theoriginal household to the individuals of the modified household based onthe main diagonal 604 of the second distance matrix 600. However, inother examples, the distance matrix generator 208 may generateadditional distance matrices. For example, the distance matrix generator208 may generate 40,320 distance matrices (e.g., every possibleremapping combination between the person identification numbers of theoriginal household and the individuals of the modified household). Inthis example, the distance matrix identifier 210 may identify thedistance matrix with the lowest reassignment distance and, thus, theperson identification assigner 212 assigns person identification numbersbased on the corresponding distance matrix.

FIG. 7A is a diagram representative of an example projected householddemographic model 700. The projected household demographic model 700includes person identification numbers 302 and a plurality of features(e.g., age bucket 304, gender 306, head of household flag 308, and longterm visitor flag 310). The projected household demographic model 700includes feature data for eight individuals (e.g., the first individual312, the second individual 314, the third individual 316, the fourthindividual 318, the fifth individual 320, the sixth individual 322, theseventh individual 324, and the eighth individual 326). In theillustrated example, the individuals of the projected householddemographic model 700 are the same individuals of the original householddemographic models 300, 400. However, the example person identificationassigner 212 separates (e.g., removes) the first individual 312 in theprojected household demographic model 700 to illustrate thereassignments defined by the second distance matrix 600 of FIG. 6 (e.g.,the person identification number 302 of the first individual 312 is notassigned to any individuals of the updated household).

FIG. 7B is a diagram representative of an example reassigned householddemographic model 750. The reassigned household demographic model 750 isthe reassigned model of the projected household demographic model 700.That is, the reassigned household demographic model 750 models the samehousehold as the modified household demographic models 350, 450. Thereassigned household demographic model 750 includes personidentification numbers 302 and the same plurality of features (e.g., agebucket 304, gender 306, head of household flag 308, and long termvisitor flag 310) as the modified household demographic models 350, 450.The reassigned household demographic model 750 includes sevenindividuals (e.g., the first individual 352, the second individual 354,the third individual 356, the fourth individual 358, the fifthindividual 360, the sixth individual 362, and the seventh individual364).

However, the person identification numbers 302 of the reassignedhousehold demographic model 750 differ from the person identificationnumbers 302 of the modified household demographic models 350, 450. Thatis, the person identification assigner 212 generates the reassignedhousehold demographic model 750 based on the example second distancematrix 600. For example, the first individual 352 (e.g., is in the 25-34age bucket, is male, is not the head of the household, and is not a longterm visitor) is assigned the person identification number 4 in thereassigned household demographic model 750. Previously (e.g., beforereassignment), the first individual 352 was assigned the personidentification number 1 in the modified household demographic models350, 450 of FIGS. 3B, 4B. FIG. 7B illustrates the household demographicmodel of the modified household after reassignment. According to thesecond distance matrix 600 of FIG. 6, the first individual 352 (e.g.,person identification number of 1) is assigned the person identificationnumber of the fourth individual 318 (e.g., the person identificationnumber of 4). Thus, the reassigned household demographic model 750illustrates the first individual 352 is assigned the personidentification number of 4. In other words, the first individual 352 ofFIG. 7B is the same first individual 352 illustrated in FIGS. 3B, 4B butwith an updated person identification number.

Flowcharts representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the household remodeler 112 ofFIGS. 1 and/or 2 are shown in FIGS. 8-9. The machine readableinstructions may be one or more executable programs or portion(s) of anexecutable program for execution by a computer processor such as theprocessor 1012 shown in the example processor platform 1000 discussedbelow in connection with FIG. 10. The program may be embodied insoftware stored on a non-transitory computer readable storage mediumsuch as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, ora memory associated with the processor 1012, but the entire programand/or parts thereof could alternatively be executed by a device otherthan the processor 1012 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowcharts illustrated in FIGS. 8-9, many other methodsof implementing the example household remodeler 112 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined. Additionally or alternatively, any or all of the blocks may beimplemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

The machine readable instructions described herein may be stored in oneor more of a compressed format, an encrypted format, a fragmentedformat, a compiled format, an executable format, a packaged format, etc.Machine readable instructions as described herein may be stored as data(e.g., portions of instructions, code, representations of code, etc.)that may be utilized to create, manufacture, and/or produce machineexecutable instructions. For example, the machine readable instructionsmay be fragmented and stored on one or more storage devices and/orcomputing devices (e.g., servers). The machine readable instructions mayrequire one or more of installation, modification, adaptation, updating,combining, supplementing, configuring, decryption, decompression,unpacking, distribution, reassignment, compilation, etc. in order tomake them directly readable, interpretable, and/or executable by acomputing device and/or other machine. For example, the machine readableinstructions may be stored in multiple parts, which are individuallycompressed, encrypted, and stored on separate computing devices, whereinthe parts when decrypted, decompressed, and combined form a set ofexecutable instructions that implement a program such as that describedherein.

In another example, the machine readable instructions may be stored in astate in which they may be read by a computer, but require addition of alibrary (e.g., a dynamic link library (DLL)), a software development kit(SDK), an application programming interface (API), etc. in order toexecute the instructions on a particular computing device or otherdevice. In another example, the machine readable instructions may needto be configured (e.g., settings stored, data input, network addressesrecorded, etc.) before the machine readable instructions and/or thecorresponding program(s) can be executed in whole or in part. Thus, thedisclosed machine readable instructions and/or corresponding program(s)are intended to encompass such machine readable instructions and/orprogram(s) regardless of the particular format or state of the machinereadable instructions and/or program(s) when stored or otherwise at restor in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C #, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example processes of FIGS. 8-9 may beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIG. 8 is a flowchart representative of example machine-readableinstructions that may be executed to implement the household remodeler112 of FIGS. 1 and/or 2. The example machine-readable instructions ofFIG. 8 begin at block 802 at which the model accessor 202 accesses thefirst household data (e.g., the original household demographic model).For example, the model accessor 202 may obtain the original householddemographic model 300 of FIG. 3A. At block 804, the example modelaccessor 202 accesses the second household data (e.g., the modifiedhousehold demographic model). For example, the model accessor 202 mayobtain the modified household demographic model 350 of FIG. 3B. Theexample model accessor 202 accesses one or more databases (e.g., thehousehold database 104, the reference database 106, etc.) for content.In some examples, the household database 104 content includes originalhousehold demographic models and modified household demographic models.Additionally, the example reference database 106 content may includemarket level data associated with household demographic models. In someexamples, the model accessor 202 accesses databases 104 and/or 106 inresponse to a household (e.g., the original household 102) beingremodeled (e.g., the modified household 102 b). In other examples, themodel accessor 202 accesses databases 104 and/or 106 at any point duringthe execution of the machine-readable instructions of FIG. 8.

At block 806, the example data formatter 204 formats data of the firstand second household. That is, the example data formatter 204 assignsthe features of the household demographic model(s) different data types.For example, the data formatter 204 assigns the age feature an intervaldata type (e.g., a range). In another example, the data formatter 204assigns the gender feature a Boolean data type.

At block 808, the example feature weight assigner 206 assigns weights tothe data of the first and second households. That is, the examplefeature weight assigner 206 assigns weights to the features of thehousehold demographic models. For example, the feature weight assigner206 may assign the gender feature a weight of 0.9. In some examples, thefeature weight assigner 206 assigns weights to the features of thehousehold demographic models based on user input. For example, a userinput may indicate to remap the household identification based on ageand, thus, the feature weight assigner 206 assigns a greater weight tothe age feature compared to other features of the household demographicmodel.

At block 810, the example distance matrix generator 208 determines thenumber of remapping combinations between the first household and thesecond household. In some examples, if the number of individuals betweenthe first and second households is N (e.g., the number of individuals inthe first and second households are equal), the distance matrixgenerator 208 determines there are N factorial (e.g., N!) number ofpossible person identification number reassignments. That is, there areN! different ways to reassign the person identification numbers of thefirst household to the individuals of the second household.

At block 812, the example distance matrix generator 208 determineswhether the number of remapping combinations exceeds a combinationthreshold. For example, the distance matrix generator 208 compares thenumber of possible person remapping assignments determined at block 810to the combination threshold. In some examples, the combinationthreshold is 2,000,000 remapping combinations. However, the combinationthreshold can be greater or less than 2,000,000. For example, in theexample described above with eight individuals in the originalhousehold, there are 40,320 remapping combinations. Thus, the distancematrix generator 208 determines the number of remapping combinationsdoes not exceed the combination threshold (e.g., 40,320<2,000,000).

If, at block 812, the distance matrix generator 208 determines thenumber of remapping combinations exceeds the combination threshold,then, at block 814, the data formatter 204 splits the first householdand/or the second household into adult households and childrenhouseholds. That is, the data formatter 204 splits the first householddemographic model into a first adult household demographic model and afirst children household demographic model. The data formatter 204 mayfurther split the second household demographic model into a second adulthousehold demographic model and a second children household demographicmodel. In examples disclosed herein, the individuals of a household aresorted into the adult or the children household based on their agerange. For example, individuals within the age range of 0-12 and 18-24may be used to generate the children household demographic model (e.g.,individuals between the ages of 0 and 24). However, in other examples,the age and/or age range used to sort individuals may be higher or lower(e.g., below 18 years old, above 18 years old, etc.). The examplehousehold remodeler 112 then proceeds to block 816.

If, at block 812, the distance matrix generator 208 determines thenumber of remapping combinations does not exceed the combinationthreshold, then, at block 816, the distance matrix generator 208generates a distance matrix for one or more possible remappingcombinations between the first and second household data. For example,the distance matrix generator 208 may generate the distance matrix 500of FIG. 5 and/or the second distance matrix 600 of FIG. 6. In someexamples, the distance matrix generator 208 generates a distance matrixfor every possible combination of person identification numberassignments between the original household and the modified household(e.g., the number of remapping combinations determined by the distancematrix generator 208 at block 810). In some examples, the distancematrix generator 208 generates children distance matrices and adultdistance matrices if the data formatter 204 generates adult and childrenhousehold demographic models (e.g., at block 814). Additional details onhow the distance matrix generator 208 generates the distance matrices isfurther described below in connection with FIG. 9.

At block 818, the example distance matrix identifier 210 identifies thedistance matrix generated by the example distance matrix generator 208representing the most similar reassignment combination (e.g., thereassignment distance matrix). In some examples, the distance matrixidentifier 210 determines the reassignment distance of each distancematrix. The distance matrix identifier 210 may determine thereassignment distance of a distance matrix is the sum of the values ofthe main diagonal. As disclosed herein, the distance matrix identifier210 identifies the distance matrix associated with the lowestreassignment distance as the reassignment distance matrix. Additionallyor alternatively, the distance matrix identifier 210 can identify adistance matrix with a reassignment distance below a reassignmentdistance threshold as the reassignment distance matrix.

At block 820, the example person identification assigner 212 assignspeople identification numbers from the first household (e.g., theoriginal household) to the second household (e.g., the modifiedhousehold) based on the reassignment distance matrix determined by theexample distance matrix identifier 210. That is, the example personidentification assigner 212 assigns the person identification number ofthe individual associated with the original household (e.g., the personidentification number of row i) to the individual associated with themodified household (e.g., the features of column j). In some examples,the person identification assigner 212 assigns the person identificationnumbers based on the main diagonal of the reassignment distance matrix(e.g., when i=j). In some examples, the person identification assigner212 generates the reassigned household demographic model 750 of FIG. 7Bat block 820. In some examples, the person identification assigner 212stores the reassigned household demographic model in the personidentification database 214.

The flowchart of FIG. 9 is representative of example machine-readableinstructions that may be executed to implement block 816 of FIG. 8. Theexample machine-readable instructions of FIG. 9 begin at block 902 atwhich the distance matrix generator 208 generates remapping rows basedon remapping costs of person feature weights. For example, a row of thedistance matrix represents the remapping cost of reassigning the i^(th)individual (e.g., a person identification number of an individual of theoriginal household) to each of the j columns (e.g., the individuals ofthe modified household). In other words, a row of the distance matrixrepresents the remapping cost of assigning the person identificationnumber of the i^(th) individual of the original household to each of thej individuals of the modified household. In examples disclosed herein,the distance matrix generator 208 determines the remapping cost for eachdistance matrix element (e.g., A_(ij)) using the Manhattan distancebetween the weighted features of the first household demographic modeland the second household demographic model.

At block 904, the example distance matrix generator 208 determines anordering of the remapping rows. That is, the distance matrix generator208 determines the row index i for each remapping row of reassignmentcosts determined by the distance matrix generator 208 at block 902. Forexample, the distance matrix generator 208 may assign a row index ibased on the person identification number of the individual of the firsthousehold demographic model (e.g., the individual with a personidentification number of 1 is assigned a row index of 1, the individualwith a person identification number of 2 is assigned a row index of 2,etc.). In some examples, the distance matrix generator 208 may randomly(e.g., in a pseudo-random number generator) assign a row index i to eachremapping row.

At block 906, the distance matrix generator 208 generates the distancematrix based on the ordering of remapping rows. For example, thedistance matrix generator 208 generates the distance matrix according tothe row indices i assigned to each remapping row. The example distancematrix generator 208 generates the distance matrix 500 of FIG. 5 and/orthe second distance matrix 600 of FIG. 6 at block 906.

At block 908, the distance matrix identifier 210 determines thereassignment distance of the distance matrix. For example, the distancematrix identifier 210 determines the sum of the distance matrix elements(e.g., the remapping costs) of the main diagonal. The distance matrixidentifier 210 determines the reassignment distance 504 of FIG. 5 andthe reassignment distance 602 of FIG. 6 at block 908.

At block 910, the distance matrix generator 208 determines whether togenerate another distance matrix. For example, the distance matrixgenerator 208 may determine to generate another distance matrix inresponse to the number of previously generated distance matrices beingless than the number of possible remapping combinations (e.g., not everyremapping combination of the first and second household has beengenerated). In some examples, the distance matrix generator 208 maydetermine to not generate another distance matrix in response to thereassignment distance of the distance matrix being less than thereassignment threshold (e.g., the distance matrix has an acceptablereassignment cost). If, at block 910, the distance matrix generator 208determines to generate another distance matrix, the distance matrixgenerator 208 returns to block 904. If, at block 910, the distancematrix generator 208 determines to not generate another distance matrix,the distance matrix generator 208 returns to block 818 of process 800 ofFIG. 8.

FIG. 10 is a block diagram of an example processor platform 1000structured to execute the instructions of FIGS. 8-9 to implement thehousehold remodeler 112 of FIGS. 1 and/or 2. The processor platform 1000can be, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 1000 of the illustrated example includes aprocessor 1012. The processor 1012 of the illustrated example ishardware. For example, the processor 1012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example household remodeler112 including the example model accessor 202, the example data formatter204, the example feature weight assigner 206, the example distancematrix generator 208, the example distance matrix identifier 210, andthe person identification assigner 212.

The processor 1012 of the illustrated example includes a local memory1013 (e.g., a cache). The processor 1012 of the illustrated example isin communication with a main memory including a volatile memory 1014 anda non-volatile memory 1016 via a bus 1018. The volatile memory 1014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1016 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1014,1016 is controlled by a memory controller.

The processor platform 1000 of the illustrated example also includes aninterface circuit 1020. The interface circuit 1020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1022 are connectedto the interface circuit 1020. The input device(s) 1022 permit(s) a userto enter data and/or commands into the processor 1012. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1024 are also connected to the interfacecircuit 1020 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 420 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1026. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1000 of the illustrated example also includes oneor more mass storage devices 1028 for storing software and/or data.Examples of such mass storage devices 1028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 1032 of FIGS. 8-9 may be stored inthe mass storage device 1028, in the volatile memory 1014, in thenon-volatile memory 1016, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that assignperson identification numbers from a first household demographic modelto individuals of a second household demographic model based ondemographic similarities between individuals. A process to determine thefeature distance (e.g., remapping cost) for every combination ofindividuals between a first household demographic model and a secondhousehold demographic model is used to identify a distance matrix withan acceptable reassignment distance (e.g., the reassignment distancedoes not exceed a reassignment threshold). As such, examples disclosedherein allow household reassignment in a manner that (a) maintains adegree of adherence to global trends while (b) reducing the deviation ofhousehold member data. The reassignment distance matrix may be used toreassign person identification numbers between the most similarindividuals between a first and second household. The disclosed methods,apparatus and articles of manufacture improve the efficiency of using acomputing device by autonomously generating and analyzing distancematrices between two households to remap person identification numbers.The disclosed methods, apparatus and articles of manufacture areaccordingly directed to one or more improvement(s) in the functioning ofa computer.

The following pertain to further examples disclosed herein. Examplemethods, apparatus, systems, and articles of manufacture to remaphousehold identification are disclosed herein. Further examples andcombinations thereof include the following:

Example 1 includes an apparatus to reduce a remapping error, theapparatus comprising a distance matrix generator to generate a distancematrix corresponding to a first household demographic model and a secondhousehold demographic model, a distance matrix identifier to determine areassignment distance of the distance matrix, and a personidentification assigner to, in response to the reassignment distancebeing less than a reassignment threshold, assign a person identificationnumber of a first person corresponding to a first household to a secondperson corresponding to a second household based on the distance matrix.

Example 2 includes the apparatus as defined in example 1, wherein thefirst household demographic model and the second household demographicmodel include at least one feature of the first person and the secondperson.

Example 3 includes the apparatus as defined in example 2, wherein the atleast one feature includes at least one of age, gender, head ofhousehold, and long term visitor.

Example 4 includes the apparatus as defined in example 2, furtherincluding a feature weight assigner to assign a first weight to a firstfeature and a second weight to a second feature, the first weight beinggreater than the second weight to define similarity between the firstand second households based on the first feature.

Example 5 includes the apparatus as defined in example 1, wherein thesecond household demographic model is a modified version of the firsthousehold demographic model.

Example 6 includes the apparatus as defined in example 1, furtherincluding a data formatter to format the data of the first householddemographic model and format the data of the second householddemographic model, the data formatted using at least one of a pluralityof ranges and Boolean labels.

Example 7 includes the apparatus as defined in example 1, wherein thedistance matrix generator is further to generate a remapping costbetween the first person of the first household and the second person ofthe second household.

Example 8 includes the apparatus as defined in example 7, wherein theremapping cost is a Manhattan distance.

Example 9 includes the apparatus as defined in example 1, wherein thereassignment distance is a sum of the main diagonal of the distancematrix.

Example 10 includes the apparatus as defined in example 1, wherein thedistance matrix is a first distance matrix and the remapping is a firstremapping, and the distance matrix generator is further to generate asecond distance matrix for a second remapping between the firsthousehold demographic model and the second household demographic model.

Example 11 includes the apparatus as defined in example 10, wherein thereassignment distance is a first reassignment distance, and the distancematrix identifier is further to determine a second reassignment distanceof the second distance matrix.

Example 12 includes the apparatus as defined in example 11, wherein theperson identification assigner is further to, in response to the firstreassignment distance being less than the second reassignment distance,assign the identification number of the first person of the firsthousehold to the second person of the second household based on thefirst distance matrix.

Example 13 includes the apparatus as defined in example 1, wherein thedistance matrix generator is further to determine a reassignmentcombination number based on a number of individuals in the firsthousehold and a number of individuals in the second household.

Example 14 includes the apparatus as defined in example 13, wherein inresponse to the distance matrix generator determining that the number ofindividuals in the first household and the number of individuals in thesecond household are equal, assign the reassignment combination numberas a factorial of the number of individuals in the first household.

Example 15 includes the apparatus as defined in example 13, wherein thedistance matrix generator is further to, in response to the reassignmentcombination number exceeding a combination threshold, generate a firstadult household demographic model, a first children householddemographic model, a second adult household demographic model, and asecond children household demographic model.

Example 16 includes the apparatus as defined in example 15, wherein thedistance matrix generator is further to generate at least one adultdistance matrix and at least one children distance matrix.

Example 17 includes a non-transitory computer readable medium comprisinginstructions that, when executed, cause at least one processor to, atleast generate a distance matrix corresponding to a first householddemographic model and a second household demographic model, determine areassignment distance of the distance matrix, and in response to thereassignment distance being less than a reassignment threshold, assign aperson identification number of a first person corresponding to a firsthousehold to a second person corresponding to a second household basedon the distance matrix.

Example 18 includes the non-transitory computer readable medium asdefined in example 17, wherein the first household demographic model andthe second household demographic model include at least one feature ofthe first person and the second person.

Example 19 includes the non-transitory computer readable medium asdefined in example 18, wherein the at least one feature includes atleast one of age, gender, head of household, and long term visitor.

Example 20 includes the non-transitory computer readable medium asdefined in example 18, wherein the instructions, when executed, furthercause the at least one processor to assign a first weight to a firstfeature and a second weight to a second feature, the first weight beinggreater than the second weight to define similarity between the firstand second households based on the first feature.

Example 21 includes the non-transitory computer readable medium asdefined in example 17, wherein the second household demographic model isa modified version of the first household demographic model.

Example 22 includes the non-transitory computer readable medium asdefined in example 17, wherein the instructions, when executed, furthercause the at least one processor to format the data of the firsthousehold demographic model and format the data of the second householddemographic model, the data formatted using at least one of a pluralityof ranges and Boolean labels.

Example 23 includes the non-transitory computer readable medium asdefined in example 17, wherein the instructions, when executed, furthercause the at least one processor to generate a remapping cost betweenthe first person of the first household and the second person of thesecond household.

Example 24 includes the non-transitory computer readable medium asdefined in example 23, wherein the remapping cost is a Manhattandistance.

Example 25 includes the non-transitory computer readable medium asdefined in example 17, wherein the reassignment distance is a sum of themain diagonal of the distance matrix.

Example 26 includes the non-transitory computer readable medium asdefined in example 17, wherein the distance matrix is a first distancematrix and the remapping is a first remapping, and the instructions,when executed, further cause the at least one processor to generate asecond distance matrix for a second remapping between the firsthousehold demographic model and the second household demographic model.

Example 27 includes the non-transitory computer readable medium asdefined in example 26, wherein the reassignment distance is a firstreassignment distance, and the instructions, when executed, furthercause the at least one processor to determine a second reassignmentdistance of the second distance matrix.

Example 28 includes the non-transitory computer readable medium asdefined in example 27, wherein the instructions, when executed, furthercause the at least one processor to, in response to the firstreassignment distance being less than the second reassignment distance,assign the identification number of the first person of the firsthousehold to the second person of the second household based on thefirst distance matrix.

Example 29 includes the non-transitory computer readable medium asdefined in example 17, wherein the instructions, when executed, furthercause the at least one processor to determine a reassignment combinationnumber based on a number of individuals in the first household and anumber of individuals in the second household.

Example 30 includes the non-transitory computer readable medium asdefined in example 29, wherein in response to determining that thenumber of individuals in the first household and the number ofindividuals in the second household are equal, assign the reassignmentcombination number as a factorial of the number of individuals in thefirst household.

Example 31 includes the non-transitory computer readable medium asdefined in example 29, wherein the instructions, when executed, furthercause the at least one processor to, in response to the reassignmentcombination number exceeding a combination threshold, generate a firstadult household demographic model, a first children householddemographic model, a second adult household demographic model, and asecond children household demographic model.

Example 32 includes the non-transitory computer readable medium asdefined in example 31, wherein the instructions, when executed, furthercause the at least one processor to generate at least one adult distancematrix and at least one children distance matrix.

Example 33 includes a method to reduce a remapping error, the methodcomprising generating, by executing an instruction with at least oneprocessor, a distance matrix corresponding to a first householddemographic model and a second household demographic model, determining,by executing an instruction with at least one processor, a reassignmentdistance of the distance matrix, and in response to the reassignmentdistance being less than a reassignment threshold, assigning, byexecuting an instruction with at least one processor, a personidentification number of a first person corresponding to a firsthousehold to a second person corresponding to a second household basedon the distance matrix.

Example 34 includes the method as defined in example 33, wherein thefirst household demographic model and the second household demographicmodel include at least one feature of the first person and the secondperson.

Example 35 includes the method as defined in example 34, wherein the atleast one feature includes at least one of age, gender, head ofhousehold, and long term visitor.

Example 36 includes the method as defined in example 34, furtherincluding assigning a first weight to a first feature and a secondweight to a second feature, the first weight being greater than thesecond weight to define similarity between the first and secondhouseholds based on the first feature.

Example 37 includes the method as defined in example 33, wherein thesecond household demographic model is a modified version of the firsthousehold demographic model.

Example 38 includes the method as defined in example 33, furtherincluding formatting the data of the first household demographic modeland format the data of the second household demographic model, the dataformatted using at least one of a plurality of ranges and Booleanlabels.

Example 39 includes the method as defined in example 33, furtherincluding generating a remapping cost between the first person of thefirst household and the second person of the second household.

Example 40 includes the method as defined in example 39, wherein theremapping cost is a Manhattan distance.

Example 41 includes the method as defined in example 33, wherein thereassignment distance is a sum of the main diagonal of the distancematrix.

Example 42 includes the method as defined in example 33, wherein thedistance matrix is a first distance matrix and the remapping is a firstremapping, further including generating a second distance matrix for asecond remapping between the first household demographic model and thesecond household demographic model.

Example 43 includes the method as defined in example 42, wherein thereassignment distance is a first reassignment distance, furtherincluding determining a second reassignment distance of the seconddistance matrix.

Example 44 includes the method as defined in example 43, furtherincluding, in response to the first reassignment distance being lessthan the second reassignment distance, assigning the identificationnumber of the first person of the first household to the second personof the second household based on the first distance matrix.

Example 45 includes the method as defined in example 33, furtherincluding determining a reassignment combination number based on anumber of individuals in the first household and a number of individualsin the second household.

Example 46 includes the method as defined in example 45, wherein inresponse to determining that the number of individuals in the firsthousehold and the number of individuals in the second household areequal, assigning the reassignment combination number as a factorial ofthe number of individuals in the first household.

Example 47 includes the method as defined in example 45, furtherincluding, in response to the reassignment combination number exceedinga combination threshold, generating a first adult household demographicmodel, a first children household demographic model, a second adulthousehold demographic model, and a second children household demographicmodel.

Example 48 includes the method as defined in example 47, furtherincluding generating at least one adult distance matrix and at least onechildren distance matrix.

Example 49 includes an apparatus to reduce a remapping error, theapparatus comprising means for generating a distance matrix to generatea distance matrix corresponding to a first household demographic modeland a second household demographic model, means for identifying areassignment distance to determine a reassignment distance of thedistance matrix, and means for person assigning to, in response to thereassignment distance being less than a reassignment threshold, assign aperson identification number of a first person corresponding to a firsthousehold to a second person corresponding to a second household basedon the distance matrix.

Example 50 includes the apparatus as defined in example 49, wherein thefirst household demographic model and the second household demographicmodel include at least one feature of the first person and the secondperson.

Example 51 includes the apparatus as defined in example 50, wherein theat least one feature includes at least one of age, gender, head ofhousehold, and long term visitor.

Example 52 includes the apparatus as defined in example 50, furtherincluding means for weight assigning to assign a first weight to a firstfeature and a second weight to a second feature, the first weight beinggreater than the second weight to define similarity between the firstand second households based on the first feature.

Example 53 includes the apparatus as defined in example 49, wherein thesecond household demographic model is a modified version of the firsthousehold demographic model.

Example 54 includes the apparatus as defined in example 49, furtherincluding means for formatting to format the data of the first householddemographic model and format the data of the second householddemographic model, the data formatted using at least one of a pluralityof ranges and Boolean labels.

Example 55 includes the apparatus as defined in example 49, wherein thedistance matrix generating means is further to generate a remapping costbetween the first person of the first household and the second person ofthe second household.

Example 56 includes the apparatus as defined in example 55, wherein theremapping cost is a Manhattan distance.

Example 57 includes the apparatus as defined in example 49, wherein thereassignment distance is a sum of the main diagonal of the distancematrix.

Example 58 includes the apparatus as defined in example 49, wherein thedistance matrix is a first distance matrix and the remapping is a firstremapping, and the distance matrix generating means is further togenerate a second distance matrix for a second remapping between thefirst household demographic model and the second household demographicmodel.

Example 59 includes the apparatus as defined in example 58, wherein thereassignment distance is a first reassignment distance, and thereassignment distance identifying means is further to determine a secondreassignment distance of the second distance matrix.

Example 60 includes the apparatus as defined in example 59, wherein theperson assigning means is further to, in response to the firstreassignment distance being less than the second reassignment distance,assign the identification number of the first person of the firsthousehold to the second person of the second household based on thefirst distance matrix.

Example 61 includes the apparatus as defined in example 49, wherein thedistance matrix generating means is further to determine a reassignmentcombination number based on a number of individuals in the firsthousehold and a number of individuals in the second household.

Example 62 includes the apparatus as defined in example 61, wherein inresponse distance matrix generating means determining that to the numberof individuals in the first household and the number of individuals inthe second household are equal, assign the reassignment combinationnumber as a factorial of the number of individuals in the firsthousehold.

Example 63 includes the apparatus as defined in example 61, wherein thedistance matrix generating means is further to, in response to thereassignment combination number exceeding a combination threshold,generate a first adult household demographic model, a first childrenhousehold demographic model, a second adult household demographic model,and a second children household demographic model.

Example 64 includes the apparatus as defined in example 63, wherein thedistance matrix generating means is further to generate at least oneadult distance matrix and at least one children distance matrix.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

The following claims are hereby incorporated into this DetailedDescription by this reference, with each claim standing on its own as aseparate embodiment of the present disclosure.

What is claimed is:
 1. An apparatus to reduce a remapping error, theapparatus comprising: a distance matrix generator to generate a distancematrix corresponding to a first household demographic model and a secondhousehold demographic model; a distance matrix identifier to determine areassignment distance of the distance matrix; and a personidentification assigner to, in response to the reassignment distancebeing less than a reassignment threshold, assign a person identificationnumber of a first person corresponding to a first household to a secondperson corresponding to a second household based on the distance matrix.2. The apparatus as defined in claim 1, wherein the first householddemographic model and the second household demographic model include atleast one feature of the first person and the second person.
 3. Theapparatus as defined in claim 2, wherein the at least one featureincludes at least one of age, gender, head of household, and long termvisitor.
 4. The apparatus as defined in claim 2, further including afeature weight assigner to assign a first weight to a first feature anda second weight to a second feature, the first weight being greater thanthe second weight to define similarity between the first and secondhouseholds based on the first feature.
 5. The apparatus as defined inclaim 1, wherein the second household demographic model is a modifiedversion of the first household demographic model.
 6. The apparatus asdefined in claim 1, further including a data formatter to format thedata of the first household demographic model and format the data of thesecond household demographic model, the data formatted using at leastone of a plurality of ranges and Boolean labels.
 7. The apparatus asdefined in claim 1, wherein the distance matrix generator is further togenerate a remapping cost between the first person of the firsthousehold and the second person of the second household.
 8. Theapparatus as defined in claim 1, wherein the distance matrix is a firstdistance matrix and the remapping is a first remapping, and the distancematrix generator is further to generate a second distance matrix for asecond remapping between the first household demographic model and thesecond household demographic model.
 9. The apparatus as defined in claim8, wherein the reassignment distance is a first reassignment distance,and the distance matrix identifier is further to determine a secondreassignment distance of the second distance matrix.
 10. The apparatusas defined in claim 9, wherein the person identification assigner isfurther to, in response to the first reassignment distance being lessthan the second reassignment distance, assign the identification numberof the first person of the first household to the second person of thesecond household based on the first distance matrix.
 11. Anon-transitory computer readable medium comprising instructions that,when executed, cause at least one processor to, at least: generate adistance matrix corresponding to a first household demographic model anda second household demographic model; determine a reassignment distanceof the distance matrix; and in response to the reassignment distancebeing less than a reassignment threshold, assign a person identificationnumber of a first person corresponding to a first household to a secondperson corresponding to a second household based on the distance matrix.12. The non-transitory computer readable medium as defined in claim 11,wherein the first household demographic model and the second householddemographic model include at least one feature of the first person andthe second person.
 13. The non-transitory computer readable medium asdefined in claim 12, wherein the instructions, when executed, furthercause the at least one processor to assign a first weight to a firstfeature and a second weight to a second feature, the first weight beinggreater than the second weight to define similarity between the firstand second households based on the first feature.
 14. The non-transitorycomputer readable medium as defined in claim 11, wherein theinstructions, when executed, further cause the at least one processor toformat first data of the first household demographic model and formatsecond data of the second household demographic model, the first andsecond data formatted using at least one of a plurality of ranges andBoolean labels.
 15. The non-transitory computer readable medium asdefined in claim 11, wherein the instructions, when executed, furthercause the at least one processor to generate a remapping cost betweenthe first person of the first household and the second person of thesecond household.
 16. The non-transitory computer readable medium asdefined in claim 11, wherein the distance matrix is a first distancematrix corresponding to a first remapping, and the instructions, whenexecuted, further cause the at least one processor to generate a seconddistance matrix for a second remapping between the first householddemographic model and the second household demographic model.
 17. Thenon-transitory computer readable medium as defined in claim 16, whereinthe reassignment distance is a first reassignment distance, and theinstructions, when executed, further cause the at least one processor todetermine a second reassignment distance of the second distance matrix.18. The non-transitory computer readable medium as defined in claim 17,wherein the instructions, when executed, further cause the at least oneprocessor to, in response to the first reassignment distance being lessthan the second reassignment distance, assign the identification numberof the first person of the first household to the second person of thesecond household based on the first distance matrix.
 19. A method toreduce a remapping error, the method comprising: generating, byexecuting an instruction with at least one processor, a distance matrixcorresponding to a first household demographic model and a secondhousehold demographic model; determining, by executing an instructionwith at least one processor, a reassignment distance of the distancematrix; and in response to the reassignment distance being less than areassignment threshold, assigning, by executing an instruction with atleast one processor, a person identification number of a first personcorresponding to a first household to a second person corresponding to asecond household based on the distance matrix.
 20. The method as definedin claim 19, further including generating a remapping cost between thefirst person of the first household and the second person of the secondhousehold.